scholarly journals Automated Storey Separation and Door and Window Extraction for Building Models from Complete Laser Scans

2021 ◽  
Vol 13 (17) ◽  
pp. 3384
Author(s):  
Kate Pexman ◽  
Derek D. Lichti ◽  
Peter Dawson
Keyword(s):  
Building Design ◽  
Point Clouds ◽  
Heritage Sites ◽  
Building Models ◽  
Significant Research ◽  
Heritage Buildings ◽  
Floor Plans ◽  
Multi Level ◽  
Dimensional Errors ◽  
Work Done

Heritage buildings are often lost without being adequately documented. Significant research has gone into automated building modelling from point clouds, challenged by irregularities in building design and the presence of occlusion-causing clutter and non-Manhattan World features. Previous work has been largely focused on the extraction and representation of walls, floors, and ceilings from either interior or exterior single storey scans. Significantly less effort has been concentrated on the automated extraction of smaller features such as windows and doors from complete (interior and exterior) scans. In addition, the majority of the work done on automated building reconstruction pertains to the new-build and construction industries, rather than for heritage buildings. This work presents a novel multi-level storey separation technique as well as a novel door and window detection strategy within an end-to-end modelling software for the automated creation of 2D floor plans and 3D building models from complete terrestrial laser scans of heritage buildings. The methods are demonstrated on three heritage sites of varying size and complexity, achieving overall accuracies of 94.74% for multi-level storey separation and 92.75% for the building model creation. Additionally, the automated door and window detection methodology achieved absolute mean dimensional errors of 6.3 cm.

scholarly journals GRAMMAR-SUPPORTED 3D INDOOR RECONSTRUCTION FROM POINT CLOUDS FOR “AS-BUILT” BIM

2015 ◽  
Vol II-3/W4 ◽  
pp. 17-24 ◽  
Author(s):  
S. Becker ◽  
M. Peter ◽  
D. Fritsch
Keyword(s):  
Point Clouds ◽  
Observation Data ◽  
Reconstruction Process ◽  
Building Model ◽  
3D Point Clouds ◽  
Building Models ◽  
Seamless Transition ◽  
Floor Plans ◽  
Knowledge Content ◽  
Realistic Geometries

The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

scholarly journals A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds

2019 ◽  
Vol 9 (14) ◽  
pp. 2904 ◽  
Author(s):  
Lei Xie ◽  
Ruisheng Wang ◽  
Zutao Ming ◽  
Dong Chen
Keyword(s):  
Point Clouds ◽  
Information Modeling ◽  
Detailed Model ◽  
Specific Data ◽  
Building Models ◽  
Structural Details ◽  
Different Types ◽  
Building Information ◽  
Floor Plans ◽  
Building Reconstruction

The automatic modeling of as-built building interiors, known as indoor building reconstruction, is gaining increasing attention because of its widespread applications. With the development of sensors to acquire high-quality point clouds, a new modeling scheme called scan-to-BIM (building information modeling) emerged as well. However, the traditional scan-to-BIM process is time-tedious and labor-intensive. Most existing automatic indoor building reconstruction solutions can only fit the specific data or lack of detailed model representation. In this paper, we propose a layer-wise method, on the basis of 3D planar primitives, to create 2D floor plans and 3D building models. It can deal with different types of point clouds and retain many structural details with respect to protruding structures, complicated ceilings, and fine corners. The experimental results indicate the effectiveness of the proposed method and the robustness against noises and sparse data.

scholarly journals Digital Modelling and Accuracy Verification of a Complex Architectural Object Based on Photogrammetric Reconstruction

Buildings ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 206
Author(s):  
Agnieszka Ozimek ◽  
Paweł Ozimek ◽  
Krzysztof Skabek ◽  
Piotr Łabędź
Keyword(s):  
Large Scale ◽  
Complex Geometry ◽  
Point Clouds ◽  
Construction Cost ◽  
Reliable Data ◽  
The Public ◽  
Heritage Sites ◽  
Object Based ◽  
Heritage Buildings ◽  
Digital Modelling

Data concerning heritage buildings are necessary for all kinds of building surveying and design. This paper presents a method for creating a precise model of a historical architectural and landscape object with complex geometry. Photogrammetric techniques were used, combining terrestrial imaging and photographs taken using UAVs. In large-scale objects, it is necessary to divide the reconstruction into smaller parts and adopt an iterative approach based on the gradual completion of missing fragments, especially those resulting from occlusions. The model developed via the reconstruction was compared with geometrically reliable data (LAS point clouds) available in the public domain. The degree of accuracy it achieved can be used in conservation, for example, in construction cost estimates. Despite extensive research on photogrammetric techniques and their applicability in reconstructing cultural heritage sites, the results obtained have not yet been compared by other researchers with LAS point clouds from the information system for land cover (ISOK).

scholarly journals STOCHASTIC AND GEOMETRIC REASONING FOR INDOOR BUILDING MODELS WITH ELECTRIC INSTALLATIONS – BRIDGING THE GAP BETWEEN GIS AND BIM

2017 ◽  
Vol IV-4/W5 ◽  
pp. 33-39
Author(s):  
Y. Dehbi ◽  
J.-H. Haunert ◽  
L. Plümer
Keyword(s):  
Graphical Models ◽  
Building Design ◽  
Point Clouds ◽  
Linear Constraints ◽  
Indoor Environments ◽  
3D Point Clouds ◽  
Building Models ◽  
Information Models ◽  
Building Information ◽  
Building Information Models

3D city and building models according to CityGML encode the geometry, represent the structure and model semantically relevant building parts such as doors, windows and balconies. Building information models support the building design, construction and the facility management. In contrast to CityGML, they include also objects which cannot be observed from the outside. The three dimensional indoor models characterize a missing link between both worlds. Their derivation, however, is expensive. The semantic automatic interpretation of 3D point clouds of indoor environments is a methodically demanding task. The data acquisition is costly and difficult. The laser scanners and image-based methods require the access to every room. Based on an approach which does not require an additional geometry acquisition of building indoors, we propose an attempt for filling the gaps between 3D building models and building information models. Based on sparse observations such as the building footprint and room areas, 3D indoor models are generated using combinatorial and stochastic reasoning. The derived models are expanded by a-priori not observable structures such as electric installation. Gaussian mixtures, linear and bi-linear constraints are used to represent the background knowledge and structural regularities. The derivation of hypothesised models is performed by stochastic reasoning using graphical models, Gauss-Markov models and MAP-estimators.

scholarly journals Seismic Damage Semantics on Post-Earthquake LOD3 Building Models Generated by UAS

2021 ◽  
Vol 10 (5) ◽  
pp. 345
Author(s):  
Konstantinos Chaidas ◽  
George Tataris ◽  
Nikolaos Soulakellis
Keyword(s):  
Point Clouds ◽  
Recovery Phase ◽  
Seismic Damage ◽  
Unmanned Aircraft ◽  
Earthquake Scenario ◽  
Geography Markup Language ◽  
3D Point Clouds ◽  
Building Models ◽  
Building Walls ◽  
Oblique Images

In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings’ seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings.

scholarly journals Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

2018 ◽  
Vol 10 (7) ◽  
pp. 1127 ◽  
Author(s):  
Pingbo Hu ◽  
Bisheng Yang ◽  
Zhen Dong ◽  
Pengfei Yuan ◽  
Ronggang Huang ◽  
...  
Keyword(s):  
Structural Model ◽  
Point Clouds ◽  
Complex Nature ◽  
Building Structures ◽  
Data Infrastructure ◽  
Roof Structure ◽  
Building Models ◽  
Topological Tree ◽  
City System ◽  
Gestalt Laws

3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains a challenging task. In this paper, a Roof Attribute Graph (RAG) method is proposed to describe the decomposition and topological relations within a complicated roof structure. Furthermore, top-down decomposition and bottom-up refinement processes are proposed to reconstruct roof parts according to the Gestalt laws, generating a complete structural model with a hierarchical topological tree. Two LiDAR datasets from Guangdong (China) and Vaihingen (Germany) with different point densities were used in our study. Experimental results, including the assessment on Vaihingen standardized by the International Society for Photogrammetry and Remote Sensing (ISPRS), show that the proposed method can be used to model 3D building roofs with high quality results as demonstrated by the completeness and correctness metrics presented in this paper.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 689-695
Author(s):  
Z. Li ◽  
W. Zhang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Traditional Approach ◽  
Holistic Approach ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Optimization Strategy ◽  
Building Roof ◽  
Building Model ◽  
Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

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